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Models/Deformable DETR

Deformable DETR

Reported on 43 benchmarks across 6 tasks · 4 papers

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Methodology33 results

  • 3DonCPPE-5
    AP50· 2021-12-15
    76.9
    best: 87.3 (Double Heads)
    CPPE-5: Medical Personal Protective Equipment DatasetarXiv:2112.09569
  • 3DonCPPE-5
    AP75· 2021-12-15
    52.8
    best: 58.8 (Localization Distillation)
    CPPE-5: Medical Personal Protective Equipment DatasetarXiv:2112.09569
  • 3DonCPPE-5
    APL· 2021-12-15
    53.9
    best: 62.6 (TridentNet)
    CPPE-5: Medical Personal Protective Equipment DatasetarXiv:2112.09569
  • 3DonCPPE-5
    APM· 2021-12-15
    35.2
    best: 43.4 (Empirical Attention)
    CPPE-5: Medical Personal Protective Equipment DatasetarXiv:2112.09569
  • 3DonCPPE-5
    APS· 2021-12-15
    36.4
    best: 45.8 (Localization Distillation)
    CPPE-5: Medical Personal Protective Equipment DatasetarXiv:2112.09569
  • 3DonCPPE-5
    box AP· 2021-12-15
    48
    best: 52.9 (TridentNet)
    CPPE-5: Medical Personal Protective Equipment DatasetarXiv:2112.09569
  • 2D ClassificationonCPPE-5
    AP50· 2021-12-15
    76.9
    best: 87.3 (Double Heads)
    CPPE-5: Medical Personal Protective Equipment DatasetarXiv:2112.09569
  • 2D ClassificationonCPPE-5
    AP75· 2021-12-15
    52.8
    best: 58.8 (Localization Distillation)
    CPPE-5: Medical Personal Protective Equipment DatasetarXiv:2112.09569
  • 2D ClassificationonCPPE-5
    APL· 2021-12-15
    53.9
    best: 62.6 (TridentNet)
    CPPE-5: Medical Personal Protective Equipment DatasetarXiv:2112.09569
  • 2D ClassificationonCPPE-5
    APM· 2021-12-15
    35.2
    best: 43.4 (Empirical Attention)
    CPPE-5: Medical Personal Protective Equipment DatasetarXiv:2112.09569
  • 2D ClassificationonCPPE-5
    APS· 2021-12-15
    36.4
    best: 45.8 (Localization Distillation)
    CPPE-5: Medical Personal Protective Equipment DatasetarXiv:2112.09569
  • 2D ClassificationonCPPE-5
    box AP· 2021-12-15
    48
    best: 52.9 (TridentNet)
    CPPE-5: Medical Personal Protective Equipment DatasetarXiv:2112.09569
  • 2D Object DetectiononCPPE-5
    AP50· 2021-12-15
    76.9
    best: 87.3 (Double Heads)
    CPPE-5: Medical Personal Protective Equipment DatasetarXiv:2112.09569
  • 2D Object DetectiononCPPE-5
    AP75· 2021-12-15
    52.8
    best: 58.8 (Localization Distillation)
    CPPE-5: Medical Personal Protective Equipment DatasetarXiv:2112.09569
  • 2D Object DetectiononCPPE-5
    APL· 2021-12-15
    53.9
    best: 62.6 (TridentNet)
    CPPE-5: Medical Personal Protective Equipment DatasetarXiv:2112.09569
  • 2D Object DetectiononCPPE-5
    APM· 2021-12-15
    35.2
    best: 43.4 (Empirical Attention)
    CPPE-5: Medical Personal Protective Equipment DatasetarXiv:2112.09569
  • 2D Object DetectiononCPPE-5
    APS· 2021-12-15
    36.4
    best: 45.8 (Localization Distillation)
    CPPE-5: Medical Personal Protective Equipment DatasetarXiv:2112.09569
  • 2D Object DetectiononCPPE-5
    box AP· 2021-12-15
    48
    best: 52.9 (TridentNet)
    CPPE-5: Medical Personal Protective Equipment DatasetarXiv:2112.09569
  • 16konCPPE-5
    AP50· 2021-12-15
    76.9
    best: 87.3 (Double Heads)
    CPPE-5: Medical Personal Protective Equipment DatasetarXiv:2112.09569
  • 16konCPPE-5
    AP75· 2021-12-15
    52.8
    best: 58.8 (Localization Distillation)
    CPPE-5: Medical Personal Protective Equipment DatasetarXiv:2112.09569
  • 16konCPPE-5
    APL· 2021-12-15
    53.9
    best: 62.6 (TridentNet)
    CPPE-5: Medical Personal Protective Equipment DatasetarXiv:2112.09569
  • 16konCPPE-5
    APM· 2021-12-15
    35.2
    best: 43.4 (Empirical Attention)
    CPPE-5: Medical Personal Protective Equipment DatasetarXiv:2112.09569
  • 16konCPPE-5
    APS· 2021-12-15
    36.4
    best: 45.8 (Localization Distillation)
    CPPE-5: Medical Personal Protective Equipment DatasetarXiv:2112.09569
  • 16konCPPE-5
    box AP· 2021-12-15
    48
    best: 52.9 (TridentNet)
    CPPE-5: Medical Personal Protective Equipment DatasetarXiv:2112.09569
  • 3DonManga109-s 15test
    COCO-style AP· uses extra data· 2021-03-25
    64.1
    best: 70.2 (YOLOX-L)
    USB: Universal-Scale Object Detection BenchmarkarXiv:2103.14027
  • 3DonWaymo 2D detection all_ns f0val
    COCO-style AP· uses extra data· 2021-03-25
    32.7
    best: 41.6 (YOLOX-L)
    USB: Universal-Scale Object Detection BenchmarkarXiv:2103.14027
  • 2D ClassificationonManga109-s 15test
    COCO-style AP· uses extra data· 2021-03-25
    64.1
    best: 70.2 (YOLOX-L)
    USB: Universal-Scale Object Detection BenchmarkarXiv:2103.14027
  • 2D ClassificationonWaymo 2D detection all_ns f0val
    COCO-style AP· uses extra data· 2021-03-25
    32.7
    best: 41.6 (YOLOX-L)
    USB: Universal-Scale Object Detection BenchmarkarXiv:2103.14027
  • 2D Object DetectiononManga109-s 15test
    COCO-style AP· uses extra data· 2021-03-25
    64.1
    best: 70.2 (YOLOX-L)
    USB: Universal-Scale Object Detection BenchmarkarXiv:2103.14027
  • 2D Object DetectiononWaymo 2D detection all_ns f0val
    COCO-style AP· uses extra data· 2021-03-25
    32.7
    best: 41.6 (YOLOX-L)
    USB: Universal-Scale Object Detection BenchmarkarXiv:2103.14027
  • 16konManga109-s 15test
    COCO-style AP· uses extra data· 2021-03-25
    64.1
    best: 70.2 (YOLOX-L)
    USB: Universal-Scale Object Detection BenchmarkarXiv:2103.14027
  • 16konWaymo 2D detection all_ns f0val
    COCO-style AP· uses extra data· 2021-03-25
    32.7
    best: 41.6 (YOLOX-L)
    USB: Universal-Scale Object Detection BenchmarkarXiv:2103.14027
  • 2D Object DetectiononSARDet-100K
    box mAP· 2020-10-08
    50
    best: 55.4 (DenoDet)
    Deformable DETR: Deformable Transformers for End-to-End Object DetectionarXiv:2010.04159

Computer Vision10 results

  • Instance SegmentationonARMBench
    AP50· 2024-07-01
    77.03
    best: 86.37 (RISE (VIT-B))
    Robot Instance Segmentation with Few Annotations for GraspingarXiv:2407.01302
  • Instance SegmentationonARMBench
    AP75· 2024-07-01
    63.4
    best: 77.51 (RISE (VIT-B))
    Robot Instance Segmentation with Few Annotations for GraspingarXiv:2407.01302
  • Object DetectiononCPPE-5
    AP50· 2021-12-15
    76.9
    best: 87.3 (Double Heads)
    CPPE-5: Medical Personal Protective Equipment DatasetarXiv:2112.09569
  • Object DetectiononCPPE-5
    AP75· 2021-12-15
    52.8
    best: 58.8 (Localization Distillation)
    CPPE-5: Medical Personal Protective Equipment DatasetarXiv:2112.09569
  • Object DetectiononCPPE-5
    APL· 2021-12-15
    53.9
    best: 62.6 (TridentNet)
    CPPE-5: Medical Personal Protective Equipment DatasetarXiv:2112.09569
  • Object DetectiononCPPE-5
    APM· 2021-12-15
    35.2
    best: 43.4 (Empirical Attention)
    CPPE-5: Medical Personal Protective Equipment DatasetarXiv:2112.09569
  • Object DetectiononCPPE-5
    APS· 2021-12-15
    36.4
    best: 45.8 (Localization Distillation)
    CPPE-5: Medical Personal Protective Equipment DatasetarXiv:2112.09569
  • Object DetectiononCPPE-5
    box AP· 2021-12-15
    48
    best: 52.9 (TridentNet)
    CPPE-5: Medical Personal Protective Equipment DatasetarXiv:2112.09569
  • Object DetectiononManga109-s 15test
    COCO-style AP· uses extra data· 2021-03-25
    64.1
    best: 70.2 (YOLOX-L)
    USB: Universal-Scale Object Detection BenchmarkarXiv:2103.14027
  • Object DetectiononWaymo 2D detection all_ns f0val
    COCO-style AP· uses extra data· 2021-03-25
    32.7
    best: 41.6 (YOLOX-L)
    USB: Universal-Scale Object Detection BenchmarkarXiv:2103.14027